BACKGROUND OF THE INVENTION
1. Field of the Invention
[0001] The present invention relates to a wiener_filter used for a reduction in a noise
of data (i.e., for an improvement in a signal to noise ratio (SNR)) and data processing.
In particular, the present invention relates to a data processing system, a data processing
method, a diagnostic imaging apparatus, and a magnetic resonance imaging apparatus
for optimally processing data depending on the noise of data and deterioration characteristics.
2. Description of the Related Art
[0002] A magnetic resonance imaging is a technique for magnetically exciting nuclear spins
in a patient (subject) located in a static magnetic field by using a high Larmor frequency
and reconstructing an image from MR signals, such as echo signals,' generated in response
to the excitation. For the magnetic resonance imaging, it is very important to improve
the SNR and spatial resolution per unit time.
[0003] A wiener_filter (WF) that was devised based on likelihood maximization of the amount
of information is available. The WF is a filter for optimizing the SNR of data defined
in Fourier space (also referred to as "k space" or "frequency space"). In theory,
an ideal_WF is defined in Fourier space and a WF aimed for only noise recovery processing
(the WF will be particularly referred to as a wiener-smoothing-filter (WSF)) is represented
as a following expression (1), where "Ps" indicates signal power (power spectrum)
and "Pn" indicates noise power.
[0004] Alternatively, if the SNR is defined as a following expression (2), the WF is represented
as a following expression (3) from the expressions (1) and (2).
[0005] The expression (3) is defined based on a variety of assumptions and a particularly
important point is that the Ps must be the signal power that does not contain the
noise. In addition, a general expression including not only the noise but also recovery
processing of deterioration, such as blur, is represented as a following expression
(4), where "H" indicates a deterioration characteristic in the filter space and "*"
indicates a complex conjugate.
[0006] However, in the actual application of the WF, when the H and the Pn are known or
measurable, the values are used as the H and the Pn. On the other hand, since an ideal
signal power (ideal_Ps) that does not contain the noise cannot be generally known,
the ideal_Ps cannot be used as the Ps. Accordingly, actual data is first measured
and noise-contaminated signal power (Ps
d) is used as the ideal_Ps to determine the WF in an approximated manner.
[0007] In addition, although the WF was originally conceived for Fourier space, it is not
only applied to frequency space, but is also applied to, for example, fresnel transform
band splitting (FREBAS) space, in which the deterioration of high frequency components
is considered to be less.
[0008] However, compared to a case in which the ideal_Ps is used, the related art is greatly
inferior in performance for an improvement in the SNR, i.e., a reduction in the noise
while maintaining a spatial frequency. In addition, for data having a smaller SNR,
the deterioration of the image quality (filtering effect) is more prominent. Thus,
when the WF is actually used, the issue is how to estimate the ideal_Ps based on the
Ps
d, which is a noise-containing processing-target data.
[0009] FIG. 37 shows a gain characteristic relative to an SNR in the ideal_WF. For a portion
where the SNR (=Ps/Pn) is large, i.e., where "Ps >> Pn" is sufficiently satisfied,
the WF is substantially equal to 1, which does not cause a large influence. On the
other hand, for a portion where the Ps approaches the Pn, the WF approaches "0" to
reduce the gain, so that the noise is optimally reduced in accordance with the sizes
of the Ps and the Pn while high-frequency components are reserved as much as possible.
[0010] Therefore, as shown in FIG. 38 (for the Ps
d with only one scan), the Ps is closer to the Pn since the gain decreases for high-frequency
components of data. Thus, with the WF, variations in high-frequency components are
more likely to affect the characteristics than low-frequency components.
SUMMARY OF THE INVENTION
[0011] The present invention has taken into consideration above-described problems, and
it is an object of the present invention to provide a data processing system, a data
processing method, a diagnostic imaging apparatus, and a magnetic resonance imaging
apparatus such that in an application to processing-target data having actual noise,
an SNR is improved while spatial frequency components of a processing-target data
are reserved as much as possible, and an adequate WF having a large improvement effect
of characteristic deterioration allows data processing to be appropriately performed.
[0012] As mentioned in claim 1 to solve the above-described problems, the present invention
provides the data processing system, comprising: a signal-power estimating unit for
estimating signal power by using reference data containing data different from processing-target
data; and a data processing unit for processing the processing-target data by using
a WF based on the signal power estimated by the signal-power estimating unit.
[0013] As mentioned in claim 7 to solve the above-described problems, the present invention
provides the data processing method, comprising steps of: (A) estimating signal power
by using reference data containing data similar to processing-target data; and (B)
processing the processing-target data by using a WF based on the signal power estimated
in the step of (A).
[0014] As mentioned in claim 24 to solve the above-described problems, the present invention
provides the diagnostic imaging apparatus, comprising: a signal-power estimating unit
for estimating signal power by using reference data containing data different from
processing-target data; a data processing unit for processing the processing-target
data by using a WF based on the signal unit estimated by the signal-power estimating
unit; an image reconstructing unit for reconstructing an image in real space by subjecting
the processing-target data, processed by the data processing unit, to two-dimensional
or three-dimensional Fourier transform for each set of data; and a synthesis and difference-operation
processing unit for performing synthesis processing and/or difference-operation processing
on the image reconstructed by the image reconstructing unit.
[0015] As mentioned in claim 27 to solve the above-described problems, the present invention
provides the magnetic resonance imaging apparatus, comprising: an image capture section
for generating magnetic resonance signals by applying gradient magnetic fields and
high-frequency pulses to a subject in a static magnetic field; a high-frequency coil
for detecting the magnetic resonance signals; an acquiring unit for arranging the
magnetic resonance signals, detected by the high-frequency coil, in frequency-band-split
space; an image generating unit for generating multiple images by reconstructing data
arranged in the frequency-band-split space by the acquiring unit; and a filter for
using, as signal power in frequency-band-split space of the processing-target image,
signal power of data in frequency-band-split space in which, an image of the multiple
images which has a highest similarity to a processing-target image, wherein a gain
is increased for a band in which an SNR is higher in the frequency-band-split space
and the gain is reduced for a band in which the SNR is lower.
[0016] Therefore, according to the present invention to provide the data processing system,
the data processing method, the diagnostic imaging apparatus, and the magnetic resonance
imaging apparatus, in the application to processing-target data having actual noise,
the SNR is improved while spatial frequency components of the processing-target data
are reserved as much as possible, and the adequate WF having the large improvement
effect of characteristic deterioration allows data processing to be appropriately
performed.
BRIEF DESCRIPTION OF THE DRAWINGS
[0017] In the accompanying drawings:
FIG. 1 is a schematic diagram showing a data processing system, a diagnostic imaging
apparatus, and a magnetic resonance imaging apparatus according to the present invention;
FIG. 2 is a block diagram showing a hardware configuration of a computer system;
FIG. 3 is a functional block diagram showing functions of a computer system for executing
a program stored on a storage device;
FIG. 4 is a flowchart for a data processing method according to the present invention;
FIG. 5 is a graph showing gain characteristics relative to an SNR, where "a" and "b"
are parameters in the WF;
FIG. 6 is a graph showing gain characteristics relative to an SNR, where "a" and "b"
are parameters in the WF;
FIG. 7 is a graph showing an improvement factor in an original data;
FIG. 8 is a graph showing an improvement factor in an original data;
FIG. 9 is a graph showing a relationship between ideal signal power (Ps) and actually
measured signal power (Psd) based on a mean_Ps system;
FIG. 10 is a figure to explain an improvement factor of a MRI image;
FIG. 11 is a figure to explain an improvement factor of a MRI image;
FIG. 12 is a figure to explain an improvement factor of a MRI image;
FIG. 13 is a figure to explain an improvement factor of a MRI image;
FIG. 14 is a figure to explain an improvement factor of a MRI image;
FIG. 15 is a figure to explain an improvement factor of a MRI image;
FIG. 16 is a schematic diagram showing an example of a case in which, in dynamic study,
an SNR of only desired data is increased and only desired data is used as reference
data;
FIG. 17 is a graph showing a group of reference data obtained by averaging and a density
change curve obtained using a contrast agent;
FIG. 18 is a diagram showing an anisotropic property of diffusion;
FIG. 19 is a view showing a diffusion weighted image (DWI) obtained by an FRWF;
FIG. 20 is a view showing a DWI obtained by an FRWF;
FIG. 21 is a view showing a DWI obtained by an FRWF;
FIG. 22 is a view showing a DWI obtained by an FRWF;
FIG. 23 is a view showing a DWI obtained by an FRWF;
FIG. 24 is a view showing a DWI obtained by an FRWF;
FIG. 25 is a view showing a DWI obtained by an FRWF;
FIG. 26 is a DWI due to differences in Ps of an FRWF;
FIG. 27 is a DWI due to differences in Ps of an FRWF;
FIG. 28 is a DWI due to differences in Ps of an FRWF;
FIG. 29 is a DWI due to differences in Ps of an FRWF;
FIG. 30 is a flip angle (FA) map due to a difference in Ps of an FRWF;
FIG. 31 is a FA-map due to a difference in Ps of a FRWF;
FIG. 32 is a FA-map due to a difference in Ps of a FRWF;
FIG. 33 is a FA-map due to a difference in Ps of a FRWF;
FIG. 34 is a graph showing an improvement factor in an SNR when the WFs are used;
FIG. 35 is a graph showing one example of a weighting function in a weighted_mean_Ps
system;
FIG. 36 is a graph showing a second modification of the weighting function in a weighted_mean_Ps
system;
FIG. 37 is a gain characteristic relative to an SNR in an ideal WF; and
FIG. 38 is a graph showing a relationship between ideal signal power Ps and actually
measured signal power (Psd) using a related art.
DESCRIPTION OF THE PREFERRED EMBODIMENTS
[0018] A data processing system, a data processing method, a diagnostic imaging apparatus,
and a magnetic resonance imaging apparatus according to the present invention will
now be described with reference to the accompanying drawings.
[0019] FIG. 1 is the schematic diagram showing the data processing system, a diagnostic
imaging apparatus, and a magnetic resonance imaging apparatus according to the present
invention.
[0020] FIG. 1 shows a diagnostic imaging apparatus, for example, a magnetic resonance imaging
(MRI) apparatus 10, for capturing images of a portion of a patient (subject) to generate
medical images. The diagnostic imaging apparatus is not limited to the magnetic resonance
imaging apparatus 10 and may be another diagnostic imaging apparatus for generating
medical images. Examples include an X-ray computerized tomography (CT) apparatus,
a single photon emission computed tomography (SPECT) apparatus, a positron emission
tomography (PET) apparatus, a PET-CT apparatus, and an ultrasonic diagnostic apparatus.
[0021] The magnetic resonance imaging apparatus 10 includes a bed portion on which a patient
P lies, a static-magnetic-field generator for generating a static magnetic field,
a gradient-magnetic-field generator for adding position information to the static
magnetic field, a transmitting/receiving section for transmitting/receiving high-frequency
signals, a controlling and computing section for controlling the entire system and
for reconstructing images, and an electrocardiogram measuring section for measuring
electrocardiograph (ECG) signals serving as signals indicating the cardiac time phase
of the patient P.
[0022] The static-magnetic-field generator includes a magnet (e.g., a superconducting magnet)
11 and a static-magnetic-field power source 12 for supplying electrical current to
the magnet 11. The magnet 11 and the static-magnetic-field power source 12 generate
a static magnetic field H
0 in the axis direction (i.e., z-axis direction) of a cylindrical opening (i.e., space
for diagnosis) into which the patient P is inserted with clearance. The magnet 11
has shim coil 24. Under the control of a computer system 16 described below, a shim-coil
power source 25 supplies electrical current for equalizing the static magnetic field
to the shim coil 24. The bed portion can be inserted into the opening in the magnet
11 so that the top plate on which the patient P lies can be retracted.
[0023] The gradient-magnetic-field generator has a gradient-magnetic-field coil unit 13
incorporated into the magnet 11. The gradient-magnetic-field coil unit 13 has three
sets (types) of coils, namely, an x-coil 13x, a y-coil 13y, and a z-coil 13z, for
generating gradient magnetic fields in an X-axis direction, a Y-axis direction, and
a Z-axis direction which are perpendicular to each other. The gradient-magnetic-field
generator is provided with a gradient-magnetic-field power source 14 for supplying
current to the x-coil 13x, the y-coil 13y, and the z-coil 13z. Under the control of
a sequencer 15 described below, the gradient-magnetic-field power source 14 supplies
pulse currents for generating gradient magnetic fields in the x-coil 13x, the y-coil
13y, and the z-coil 13z.
[0024] The gradient-magnetic-field power source 14 controls the pulse current to be supplied
to the x-coil 13x, the y-coil 13y, and the z-coil 13z to combine the gradient magnetic
fields in three physical axes (i.e., the x-axis, y-axis, and z-axis) directions. This
makes it possible to arbitrarily set or change logic-axis directions including a slice-direction
gradient magnetic field G
S, a phase-encoding direction gradient magnetic field G
E, and a read-direction (frequency encoding direction) gradient magnetic field G
R, which are perpendicular to each other. The gradient magnetic fields in a slice encoding
(SE) direction, in a phase encoding (PE) direction, and in a read out (RO) direction
are superimposed on the static magnetic field H
0.
[0025] The transmitting/receiving section has an RF coil 17, which is provided adjacent
to the patient P in the image-capture space in the magnet 11, and a transmitter 18T
and a receiver 18R, which are connected to the RF coil 17. The transmitter 18T and
the receiver 18R operate under the control of the sequencer 15 described below. Through
the operation, the transmitter 18T supplies an RF current pulse having a Larmor frequency
for exciting nuclear magnetic resonance (NMR) to the RF coil 17. The receiver 18R
receives MR signals (high-frequency signals) received by the RF coil 17; performs
various types of signal processing, such as preamplification, intermediate-frequency
conversion, phase detection, low-frequency amplification, and filtering; and them
performs A/D conversion on the resulting signals to generate digital echo data of
the MR signals (the data is also referred to as "original data" or "raw data").
[0026] In addition, the controlling and computing section includes the sequencer 15 (also
referred to as a "sequence controller"), the computer system 16, a display device
22, an input device 23, and an audio generator 26. The computer system 16 has functions
for issuing a pulse-sequence information instruction to the sequencer 15 and managing
the entire operation of the magnetic resonance imaging apparatus 10. The controlling
and computing section may have a drive 27 to which a storage medium 27a is attachable/detachable.
[0027] The sequencer 15 includes a CPU and a memory, which are not shown. The sequencer
15 is configured to store the pulse sequence information sent from the computer system
16, to control the operations of the gradient-magnetic-field power source 14, the
transmitter 18T, and the receiver 18R in accordance with the stored information, to
temporarily receive the MR-signal echo data output from the receiver 18R, and to transfer
the echo data to the computer system 16. The pulse sequence information used herein
refers to all information required for causing the gradient-magnetic-field power source
14, the transmitter 18T, and the receiver 18R to operate in accordance with a series
of pulse sequences. Examples of the pulse sequence information include the intensity
of pulse current applied to the x-coil 13x, the y-coil 13y, and the z-coil 13z; the
application period of time; and the application timing.
[0028] FIG. 2 is a block diagram showing the hardware configuration of the computer system
16.
[0029] As shown in FIG. 2, the computer system 16 is implemented with hardware including
a central processing unit (CPU) 41, a memory 42, a hard disk (HD) 44, and so on. The
CPU 41 is interconnected with each hardware element, included in the computer system
16, through a bus B that serves as a common signal-transmission path.
[0030] The CPU 41 serves as a controller for controlling the entire computer system 16.
The CPU 41 loads, to the memory 42, a program stored on the HD 44 or a program read
from the storage medium 27a, which is attached to the drive 27, and installed on the
HD 44, and then executes the program.
[0031] The memory 42 also has elements such as a read only memory (ROM) and a random access
memory (RAM), and serves as a storage medium that is used to store initial program
loading (IPL), basic input/output system (BIOS), and data, that is used to temporarily
store data, and that is also used as a work memory for the CPU 41.
[0032] The HD 44 is implemented with a nonvolatile semiconductor memory or the like. The
HD 44 serves as a storage medium for storing programs (including an OS and so on,
as well as application programs) installed on the computer system 16, MR-signal echo
data transferred from the sequencer 15, and data of reconstructed images and so on.
[0033] FIG. 3 is a functional block diagram showing the functions of the computer system
16 for executing a program stored on the storage device, such as the HD 44.
[0034] When the CPU 41 (shown in FIG. 2) reads and executes a program, the computer system
16 serves as a data acquiring unit 51, a data arranging unit 52, a signal-power estimating
unit 53, a data processing unit 55, an image-reconstructing unit 56, a synthesis and
difference-operation processing unit 57, an image recording unit 58, and a display
controlling unit 59. That is, the computer system 16 serves as a data processing system.
[0035] The data acquiring unit 51 has a function for acquiring the echo data transferred
from the sequencer 15 upon the execution of scanning.
[0036] The data arranging unit 52 has a function for arranging the echo data, acquired by
the data acquiring unit 51, in Fourier space (also referred to as "k space" or "frequency
space") in the HD 44 (shown in FIG. 2).
[0037] The signal-power estimating unit 53 has a function for estimating signal power (power
spectrum, Ps) by using data that is different from processing-target data arranged
in the Fourier space.
[0038] The data processing unit 55 has a function for processing the processing-target data,
arranged in the Fourier space, by using a WF based on the Ps estimated by the signal-power
estimating unit 53.
[0039] The image-reconstructing unit 56 has a function for reconstructing, for each set
of the processing-target data processed by the data processing unit 55, an image in
real space by subjecting the processing-target data to two-dimensional or three-dimensional
Fourier transform.
[0040] The synthesis and difference-computation processing unit 57 has a function for performing
synthesis processing and/or difference-operation processing on the image reconstructed
by the image-reconstructing unit 56. The synthesis processing involves addition processing
for each pixel, maximum intensity projection (MIP) processing, and so on. As another
example of the synthesizing processing, the axes of multiple frames may be aligned
in Fourier space to synthesis one-frame raw data by directly using raw data. Examples
of the addition processing includes simple addition processing, addition average processing,
and weighted addition processing.
[0041] The image recording unit 58 has a function for recording, on the HD 44 (shown in
FIG. 2), not only reconstructed images but also images subjected to the aforementioned
synthesis processing and the difference-operation processing.
[0042] The display controlling unit 59 has a function for supplying the image, subjected
to the image processing performed by the synthesis and difference-operation processing
unit 57, to the display device 22 and causing the display device 22 to display device
the image as an MRI image.
[0043] The display device 22 shown in FIG. 1 is used to display, for example, a reconstructed
image. Information desired by an operator, such as parameter information, scan conditions,
a pulse sequence, and information regarding the image synthesis and difference operation,
can be input to the computer system 16 via the input device 23.
[0044] Upon receiving an instruction from the computer system 16, the audio generator 26
can output voice messages for starting breath holding and ending breath holding.
[0045] The portable storage medium 27a, such as a flexible disk (FD), a compact disc read
only Memory (CD-ROM), a magneto optical (MO) disk, a digital versatile disc (DVD),
a magnetic disc, and a semiconductor memory, can be attached to/detached from the
drive 27. The drive 27 reads data (including a program) recorded on the storage medium
27a and outputs the read data to the computer system 16, or writes data, supplied
from the computer system 16, to the storage medium 27a. The program executed by the
CPU 41 can be temporarily or permanently stored (recorded) on the storage medium 27a.
Such a storage medium 27a can be supplied as the so-called "package software".
[0046] The electrocardiogram measuring section includes an ECG (electrocardiogram) sensor
27 and an ECG unit 28. The ECG sensor 27 is attached to the body surface of the patient
P to detect ECG signals as electrical signals. The ECG unit 28 performs various types
of processing, including digitization processing, on the sensor signals and outputs
the resulting signals to the computer system 16 and the sequencer 15. This arrangement
allows for data acquisition based on electrocardiographic synchronization. The ECG
sensor 27 and the ECG unit 28 serve as means for detecting information indicating
a cardiac time phase.
[0047] A data processing method according to the present invention will now be described
with reference to the flow chart shown in FIG. 4.
[0048] When the patient P lies on the bed portion, the static-magnetic-field power source
12 supplies current to the magnet 11 to form a static magnetic field in the magnet
11. Also, the shim-coil power source 25 supplies current to the shim coil 24 to equalize
the static magnetic field formed in the image-capture area.
[0049] The input device 23 gives an operation instruction, together with sequence-selection
information, to the computer system 16. Thus, the computer system 16 supplies a pulse
sequence to the sequencer 15. In accordance with the pulse sequence received from
the computer system 16, the sequencer 15 drives the gradient-magnetic-field power
source 14, the transmitter 18T, and the receiver 18R to form an X-axis gradient magnetic
field, a Y-axis gradient magnetic field, and a Z-axis gradient magnetic field in the
image-capture area and to generate RF signals.
[0050] In this case, the X-axis gradient magnetic field, the Y-axis gradient magnetic field,
and the Z-axis gradient field formed in the gradient magnetic-field coil are mainly
used as a gradient magnetic field for the phase encoding, a gradient magnetic field
for the read out, and a gradient magnetic field for the slice encoding, respectively.
Thus, the direction of the spins of atomic nuclei exhibits regularity and the X coordinate
and Y coordinate, which are two-dimensional position information, at a slice formed
in the Z-axis direction by the gradient magnetic field for the slice encoding are
converted by the gradient magnetic field for the phase encoding and the gradient magnetic
field for the read out into the amount of phase change in the spins of atomic nuclei
inside the patient P and the amount of frequency change, respectively.
[0051] In accordance with the pulse sequence transmitted from the transmitter 18T, RF signals
are applied to the RF coil 17. The RF signals are then transmitted from the RF coil
17 to the patient P. In addition, in accordance with the frequency of the RF signals,
nuclear magnetic resonance of atomic nuclei contained in a slice occurs to cause NMR
signals to be generated. The NMR signals are then received by the RF coil 17 and are
sent to the receiver 18R.
[0052] Upon receiving the NMR signals from the RF coil 17, the receiver 18R executes various
types of signal processing, such as preamplification, intermediate-frequency conversion,
phase detection, low-frequency amplification, and filtering. The receiver 18R further
performs A/D conversion on the NMR signals to generate echo data, which is digital
data of the NMR signals. The echo data generated by the receiver 18R is transferred
to the computer system 16.
[0053] The data acquiring unit 51 of the computer system 16 acquires the echo data transferred
from the sequencer 15 upon the execution of scanning (step S1).
[0054] Subsequently, the data arranging unit 52 arranges the echo data, acquired by the
data acquiring unit 51, in an entire multi-resolution space, such as Fourier space
or fresnel transform band splitting (FREBAS) space, in the storage medium 27a or the
storage device, such as the HD 44 shown in FIG. 2 (step S2). The following description
is given of an example in which the echo data are arranged in entire Fourier space
in the HD 44, unless otherwise particularly stated.
[0055] The signal-power estimating unit 53 estimates the signal power by using reference
data containing data that is different from the echo data (i.e., processing-target
data) arranged in the Fourier space (step S3).
[0056] By using a WF based on the signal power estimated by the signal-power estimating
unit 53, the data processing unit 55 processes the processing-target data arranged
in the Fourier space (step S4).
[0057] The image-reconstructing unit 56 subjects the processing-target data, processed by
the data processing unit 55, to two-dimensional or three-dimensional Fourier transform
for each set of data to reconstruct an image in the real space (step S5).
[0058] The synthesis and difference-operation processing unit 57 performs synthesis processing
and/or difference-operation processing on the image reconstructed by the image-reconstructing
unit 56 (step S6). The synthesis processing involves addition processing for each
pixel, maximum intensity projection processing, and so on. As another example of the
synthesizing processing, the axes of multiple frames may be aligned in Fourier space
to synthesis one-frame raw data by directly using raw data. Examples of the addition
processing include simple addition processing, addition average processing, and weighted
addition processing.
[0059] The image recording unit 58 records, on the storage medium 27a or the storage device
such as the HD 44, not only reconstructed images but also images subjected to the
aforementioned synthesis processing and/or difference-operation processing (step S7).
[0060] The display controlling unit 59 supplies the image, subjected to the image processing
performed by the synthesis and difference-operation processing unit 57, to the display
device 22, and the display device 22 display devices the supplied image as an MRI
image (step S8).
[0061] The data processing method of the present invention has a feature in the signal power
estimating method in step S3. The signal power estimating method will now be described.
[0062] First of all, a typical WF was devised based on the principle of likelihood maximization
of the amount of information, and serve as filters for optimizing the SNR of data
defined in frequency space. In theory, a WF is defined in Fourier space and a WF intended
for only noise recovery processing (the WF will be particularly referred to as a wiener-smoothing-filter
(WSF)) is represented as a following expression (5), where "Ps" indicates the signal
power and "Pn" indicates noise power.
[0063] Alternatively, if the SNR is defined as a following expression (6), the WF is represented
as a following expression (7) from the expressions (5) and (6).
[0064] The expression (7) is defined based on a variety of assumptions and a particularly
important point is that the Ps must be the signal power that does not contain the
noise. In addition, a general expression including not only the noise but also recovery
processing of deterioration, such as blur, is represented as a following expression
(8), where "H" indicates a deterioration characteristic in the filter space and "*"
indicates a complex conjugate.
[0065] However, in the actual application of the WF, when the H and the Pn are known or
measurable, the values are used as the H and the Pn. On the other hand, since an ideal
signal power (ideal_Ps) that does not contain the noise cannot be generally known,
the ideal Ps_cannot be used as the Ps. Accordingly, actual data is first measured
and noise-contaminated signal power (Ps
d) is used as the ideal_Ps to determine the WF in an approximated manner.
[0066] In this case, in order to distinguish from the ideal form, the signal power, the
noise power, and the SNR measured from actual data are indicated by Ps
d, Pn
d, and SNR
d, respectively. The following description is given on the assumption that the H is
not corrected and the WSF intended for an improvement in the SNR is used as the WF;
however, the present invention is also applicable to a case in which the H is corrected.
[0067] For data in one two-dimensional Fourier space, actually measured the signal power
Ps
d is expressed by a spatial-frequency function Ps
d (k
x, k
y). Also, since the actually measured the signal power Pn
d can be regarded as being constant in the Fourier space, measurement is performed
by averaging multiple coordinates of high frequency components in which the noise
components with which the Ps
d can be ignored are dominant.
[0068] Now, some modified examples, using actual data, for determining the WF will now be
described. When a parameter for controlling the estimated ratio of the noise power
is indicated by "a", and the Ps is expressed by max [0, Ps
d - a × Pn], the following two expressions are given as a following expression (8)
or (9). For "a = 1" and "Ps
d > Pn
d" in the expressions (9), it becomes equal to the theoretical expression.
[0069] When a parameter "b" for controlling a WF characteristic relative to the SNR is used,
the following is represented as a following expression (11). For further generalization,
the expression (11) is applied to the expression (10) and the WF is represented as
a following expression (12). The expressions (9) and (10) correspond to performing
threshold processing for bringing, of the Ps
d of measured actual data, noise components and components smaller than noise to zero,
and the parameters "a" and "b" are coefficients provided for performing adjustment
for a case in which an actual coefficient for controlling the noise and a least squares
condition do not necessarily match each other.
[0070] FIGS. 5 and 6 are graphs showing gain characteristics relative to the SNR, where
"a" and "b" are parameters in the WF in the expression (12). FIG. 5 is graph showing
a gain characteristic relative to the SNR, where "(a, b) = (1, 1), (1, 2), and (1,
3)" in the WF in the expression (12). FIG. 6 is a graph showing a gain characteristic
relative to the SNR, where "(a, b) = (1, 1), (1, 2), and (1, 3)" in the WF in the
expression (12).
[0071] FIGS. 7 and 8 are graphs showing improvement factors in the original data.
[0072] FIG. 7 is a graph showing an improvement factor in the original data in the case
of a different_Ps system in which reference data containing data different from processing-target
data is used to estimate the Ps. Specifically, FIG. 7 is a graph showing an improvement
factor in the original data in the case of a mean_Ps system in which only data similar
to processing-target data is used as reference data and the Ps
d obtained by averaging (smoothing) adjacent data points is estimated as the Ps. On
the other hand, FIG. 8 is a graph showing an improvement factor in the original data
in the case of a same_Ps system in which the Ps
d obtained by averaging adjacent data points is estimated as the Ps based on processing-target
data.
[0073] More specifically, FIG. 7 shows a Ps_SNRR ("SNR of ideal_Ps/ SNR of Ps
d") versus RMSER (each root mean quare error (RMSE) after filtering processing/RMSE
before filtering processing) based on RMSE, which is an index for error relative to
the ideal data, in the case of the mean_Ps system. In this case, the Ps_SNRRs are
1.0, 1.4, 2.0, 2.5, 5.0 and 10.0. FIG. 8 shows the Ps_SNRR versus RMSER in the case
of the same_Ps system, where the Ps_SNRRs are 1.0, 1.7, 2.2, 2.6, 3.0, and 3.6.
[0074] With respect to each of the mean_Ps system and the same_Ps system, the values were
plotted for a case in which filter processing is performed with a WF (FTWF) in typical
FT space and a case in which filter processing is performed with a WF (FRWF) in FREBAS
space. With respect to the FTWF and the FRWF, the plotting was performed separately
for a type W1 for threshold processing and a type W2 that is an ideal type. It can
be understood from the figures that an improvement in the SNR of data used as the
Ps is effective for improving the SNR of the processing-target data.
[0075] It is clear from FIGS. 7 and 8 that the RMSER in the case of the mean_Ps system is
smaller than that in the case of the same_Ps system and is thus a superior improvement
in the SNR. It is also clear that the FRWF has a smaller RMSER in the filter application
space than the FTWF and is thus a superior improvement in the SNR. For both the FTWF
and the FRWF, as a basic filter, the type W1 is preferable for "Ps_SNRR < 2" and the
type W2 is preferable for "Ps_SNRR > 2".
[0076] In addition, for the optimum value of the Ps_SNRR, which is a ratio of an improvement
factor in the SNR to data used for the Ps in the present invention, about 2 (or 4
in the number of acquisition (NAQ)) is enough for the type W1 and about 5 (or 25 in
NAQ) is enough for the type W2. Thus, an effect obtained by further increasing the
value is small. The present invention requires, in addition to processing-target data,
other data (i.e., reference data) having a similar distribution in the filter application
space and having a relatively large SNR. However, for example, for application to
a case in which the same portion is repeatedly scanned, such as dynamic scanning,
there is no need to additionally acquire data similar to processing-target data in
order to estimate the Ps. Thus, this method is very effective method depending on
an actual application.
[0077] FIG. 9 is a graph showing a relationship between the ideal signal power (Ps) and
actually measured a signal power (Ps
d) based on the mean_Ps system. Compared to the graph in FIG. 38, it is clear that
Ps
d in the graph in FIG. 9 is close to the Ps and thus the Ps
d using the mean_Ps system is effective to improve the SNR.
[0078] FIGS. 10 to 15 show figures to explain an improvement factor of MRI images.
[0079] FIG. 10 shows an MRI image ("RMSE = 0.3785" and "SNR = 1/64.9") provided by data
containing the noise without recovery processing. FIG. 11 shows an ideal MRI image
("RMSE = 0" and "SNR = 1/11.8") that contains no noise. FIG. 12 shows an MRI image
("RMSE = 0.2477" and "SNR = 1/40.2") provided by data subjected to recovery processing
using an FRWF based on the same_Ps system. FIG. 13 shows an MRI image ("RMSE = 0.1345"
and "SNR = 1/6.7") provided by data subjected to recovery processing using an FRWF
based on the ideal_Ps. FIG. 14 shows an MRI image ("RMSE = 0.2642" and "SNR = 1/45.1")
provided by data subjected to recovery processing using an FTWF based on the same_Ps
system. FIG. 15 shows an MRI image ("RMSE = 0.1693" and "SNR = 1/23.1") provided by
data subjected to recovery processing using an FTWF based on the ideal_Ps.
[0080] Comparison between the MRI image shown in FIG. 13 and the MRI image shown in FIG.
15 shows that the MRI image (shown in FIG. 13) subjected to recovery processing using
the FRWF is superior in image quality and is a superior improvement factor in the
SNR. Comparison of the MRI image shown in FIG. 11 with the MRI images shown in FIGS.
10, 12, 13, and 14 shows that the MRI image (shown in FIG. 13) subjected to recovery
processing using the FRWF based on the ideal_Ps is closest to the MRI image shown
in FIG. 11.
[0081] The present invention relates to a method and application for specifically realizing
the different_Ps system (e.g., the mean_Ps system) for estimating the Ps by using
reference data containing data different from processing-target data. When the different_Ps
system is used, it is ideal to use the same parameters for the data different from
processing-target data. However, it is not necessarily realistic to additionally obtain
data, different from processing-target data, for estimating the Ps. With respect to
a variation permissible range of the Ps used for the WF, the aforementioned robustness
for low frequency components can be used. Examples (1) to (7) in which the characteristic
is taken advantage of will be described below.
(1) Dynamic Study
[0082] In dynamic study in which the same processing-target data are acquired in a time
series, after a desired point of time at which, for example, a contrast agent is injected,
data before the desired point of time is acquired as reference data with a high SNR.
On the other hand, data after the desired point of time is acquired as processing-target
data, with a reduced SNR to improve the temporal resolution. In this case, only the
data before the desired point of time can be used as the reference data to estimate,
as the Ps, the Ps
d obtained by averaging adjacent data points. In this case, the difference between
a time axis of the Ps
d and the reference data can be a problem, and thus must be small enough to be negligible
for application. In the contrast-imaging effect, a relatively large frequency change
does not occur in high-frequency components, compared to low frequency components.
[0083] Even with the WF, the low frequency components are robust compared to the high-frequency
components. Thus, the WF can be useful for data whose high-frequency components of
signals do not change much. There is also a method using the property (i.e., a method
in which only low-frequency components are acquired with respect to processing-target
data after a desired point of time and are combined with high-frequency components
of reference data before the desired point of time).
(1-a) Case in which the SNR of only reference data is increased and the reference
data is used
[0084] FIG. 16 is a schematic diagram showing an example of a case in which, in dynamic
study, the SNR of only desired data is increased and only the desired data is used
as reference data. In this case, when the acquisition times of respective scans are
the same, the Ps
d (S(base)) is obtained from a following expression by averaging based on a group of
reference data (S(base
i)) acquired before the injection of a contrast agent. Alternatively, NAQ is increased
from the beginning for an MRI examination or an RI examination or the dose is increased
for a CT examination to acquire the group of reference data.
[0085] FIG. 17 is a graph showing the group of reference data obtained by averaging and
a density change curve obtained using a contrast agent. In this case, "ΔS(t
j) = S(t
j) - S(base)" is satisfied.
(1-b) Case in which all data are averaged and the averaged data is used
[0086] The mean_Ps system is not limited to a case in which only data acquired before the
injection of a contrast agent is used as reference data and Ps
d of the reference data is estimated as the Ps. The mean_Ps system may be used in a
case in which all time-series data that also include processing-target data acquired
after the injection of the contrast agent are averaged, the averaged data is used
as reference data, and the Ps
d thereof is estimated as the Ps. Data obtained from an area where a movement or position
shift occurs or data after correction are used, if needed.
[0087] In case (1-b), the SNR improving effect of the WF is larger in case (1-b) than case
(1-a), since the SNR of the Ps
d of the reference data improves. On the other hand, the possibility that differences
in motion and contrast are affected is larger in case (1-b) than in case (1-a).
(2) For functional-MRI (f-MRI)
[0088] In an f-MRI, multiple pieces of data are acquired over time while ON/OFF states of
the load are repeated at regular intervals. Since this is basically the same as in
the case of the dynamic study, the same method can essentially be used. The reference
data may be acquired with an increased NAQ and the Ps
d of the reference data may be used for the time-axis data after and before the load
application. In a more practical sense, the f-MRI acquires two sets of data having
different contrasts from each other after and before the load application, the Ps
d may be divided into two types of the Ps
d, namely, Ps
d [1] of first reference data composed of only data before the load application and
Ps
d [2] of second reference data composed of only data after the load application. Compared
to a case in which the average of all data after and before the load application is
used, the SNR of the Ps
d decreases to 1/√2, but Ps
d difference caused by a difference in contrast can be minimized.
(3) Data after image standardization
[0089] It is possible to use the same types of parameter data even for different examinations.
When an image is modified as ideal data and is standardized, standardized data can
be used for any patient's data. Further, since any past data of the same patient can
be used, it is not necessary to further acquire ideal data.
(4) Images of different types of parameter for the same patient
[0090] It is also possible to use image data of different types of parameter for the same
patient, the data being acquired in the same examination session. A parameter with
which the SNR is better is used for Ps. For the MRI examination, T1 weighting (T1W)
can be used for T2 weighting (T2W) and T2W or the like can be used for a fluid attenuated
inversion recovery (FLAIR).
(5) Before and after contrast imaging
[0091] For contrast imaging, an image-capture operation is generally performed once before
the contrast imaging and once after the contrast imaging for the same examination
session. In the mean_Ps system in this case, with respect to data before the contrast
imaging, only data before the contrast imaging is used as reference data and the Ps
d of the reference data is estimated as the Ps, and with respect to data after the
contrast imaging, reference data is obtained by also adding data before the contrast
imaging to data after the contrast imaging and the Ps
d of the reference image is estimated as the Ps. Even when the two types of data have
the same SNR, the data after the contrast imaging satisfies "Ps_SNRR = sqrt (2) =
1.4", thereby improving the SNR.
(6) Signals that change over time
[0092] For digitalized data (audio and moving images) that change over time, the Ps is estimated
using the Ps
d of reference data obtained by averaging data in a fixed time and is applied to processing-target
data that are generated and that have a similar Ps distribution. Alternatively, a
portion that does not change much over time is extracted and the Ps is applied to
only the portion. In particular, for filtering in Fourier space, a translation in
the Fourier space is a change in phase in the Fourier space, and thus, a change in
Ps corresponding to the square of a signal value does not occur. Thus, the translation
is robust relative to a certain level of motion. However, although the SNR improves
by the use of a mere frame average, variations occur when there is motion.
(7) For diffusion tensor imaging (DTI)
[0093] This example shows an application to DTI data and data in motion probing gradient
(MPG) axes are averaged to reduce residual error of random noise.
[0094] FIG. 18 is a diagram showing an anisotropic property of diffusion.
[0095] The orientations of nerve fibers match each other in a longitudinal-axis direction
in which the diffusion is large. λ1, λ2, and λ3 indicate the sizes of diffusion coefficients
in decreasing order of size and V1, V2, and V3 indicates directions. Six parameters
are computed for each voxel, and a parameter "flip angle (FA)" indicating an anisotropic
property, tractography that expresses nerves by tracking the vector V1 in the longitudinal
axis, and so on are imaged.
[0096] FIGS. 19 to 25 are views showing diffusion weighted images (DWIs) obtained by an
FRWF.
[0097] When the MPG direction is expressed by b (x, y, z), FIG. 19 shows a DWI in b0 (0,
0, 0), FIG. 20 shows a DWI in b1 (1, 1, 0), FIG. 21 shows a DWI in b2 (1, -1, 0),
FIG. 22 shows a DWI in b3 (0, 1, 1), FIG. 23 shows a DWI in b4 (0, 1, -1), FIG. 24
shows a DWI in b5 (1, 0, 1), and FIG. 24 shows a DWI in b6 (-1, 0, 1).
[0098] FIGS. 26 to 29 show DWIs due to differences in Ps of the FRWF.
[0099] FIG. 26 shows an original DWI, which has not been subjected to noise recovery processing.
FIG. 27 shows a DWI subjected to recovery processing using a FRWF based on the same_Ps.
FIG. 28 shows a DWI subjected to recovery processing using a FRWF based on the mean_Ps.
FIG. 29 shows a DWI subjected to recovery processing using a FRWF based on the ideal_Ps.
[0100] FIGS. 30 to 33 show FA maps due to a difference in Ps of the FRWF.
[0101] The images shown in FIGS. 30 to 33 are created from seven sets of images shown in
FIGS. 19 to 25.
[0102] Specifically, the FA map shown in FIG. 30 has not been subjected to noise recovery
processing, the FA map shown in FIG. 31 was subjected to recovery processing using
a FRWF based on the same_Ps, the FA map shown in FIG. 32 was subjected to recovery
processing using a FRWF based on mean_Ps, and the FA map shown in FIG. 33 was subjected
to recovery processing using a FRWF based on the ideal_Ps.
[0103] FIG. 34 is a graph showing an improvement factor in the SNR when the WFs are used.
[0104] It can been seen that, compared to the same_Ps system, the mean_Ps system improves
the RMSE, which is a numerical index indicating a difference between a visual image
and an ideal image, to a level comparable to the case in which the ideal_Ps is used.
The mean_Ps system is very effective method to readily improve the SNR of DTIs.
[0105] Compared to a typical method using the Ps
d of processing-target data as Ps, examples (1) to (7) described above can further
improve the SNR of processing-target data while minimizing deterioration of the spatial
resolution.
[0106] Examples (1) to (7) can not only be applied to typical Fourier space but also be
applied to any space in which the frequency band is split, such as FREBAS space.
[0107] In addition, even compared to the same_Ps system in which processing-target data
are smoothed to reduce the noise components of Ps, the mean_Ps system can reduce RMSE,
which is an index indicating an error relative to ideal data. Further, optimum conditions
of the parameters do not depend on data. Thus, in the mean_Ps system, when the Ps_SNRR
of reference data used as the Ps of the WF satisfies a predetermined criterion, the
RMSE is saturated and thus the robustness for condition setting is large. Thus, it
is possible to use a WF equation based on the theory that the Ps does not contain
noise when a predetermined criterion of the Ps_SNRR is satisfied. Thus, since a scheme
in which the Ps_SNRR of the reference data is measured and is compared with a predetermined
criterion for the Ps_SNRR to thereby select an optimum filter form is provided, automation
is possible.
[0108] Typically, in the different_Ps system, data different from processing-target data
is required. For application to cases in which multiple pieces of data are acquired
for the same target, for example, for cases in which data of different parameters
in T1 weighting (T1W), T2W, proton W, and so on in dynamic study, load testing (such
as f-MRI), and MRI are acquired, it is sufficient to determine the data different
from processing-target data by averaging already available data, which does not require
additional acquisition of reference data for estimating the Ps.
[0109] Basic data used for the Ps estimation in the present invention may be generated by
a diagnostic imaging apparatus that is different from the diagnostic imaging apparatus
in which processing-target data is generated. For example, when the magnetic resonance
imaging apparatus 10 is used to generate processing-target data regarding a desired
portion, the diagnostic imaging apparatus, such as an X-ray CT apparatus, a SPECT
apparatus, a PET apparatus, a PET-CT apparatus, and an ultrasonic diagnostic apparatus,
is used with respect to a portion equivalent to the desired portion to generate reference
data.
[0110] In the present embodiment, the mean_Ps system has been described as one example of
the different_Ps system for estimating the Ps
d as the Ps by using reference data containing data different from processing-target
data. However, the different_Ps system in the present invention may be a weighted_mean_Ps
system in which the Ps
d obtained by weighted-averaging of adjacent data points is estimated as the Ps based
on reference data similar to processing-target data. In the weighted_mean_Ps, weighing
of processing-target data is increased for low-frequency components. On the other
hand, for high-frequency components, weighting of either one of processing-target
data and data different from the processing-target data, the either one having a larger
SNR of the Ps
d, is increased.
[0111] With Ps
d based on the mean_Ps system, an improvement effect in the SNR is large for high-frequency
components, but is small for low-frequency components. Low-frequency components define
rough contrast, and thus, in low-frequency components, when there is a difference
between Ps
d (Ps
d [mean_Ps]) determined by the mean_Ps system and Ps
d (Ps
d [same_Ps]) determined by the same_Ps system, the low-frequency characteristics of
the WF deteriorate. Thus, when the mean_Ps system is used as the different_Ps system,
Ps
d [mean_Ps] may results in a case in which the WF cannot be optimally estimated compared
to the ideal_Ps.
[0112] Accordingly, in order to prevent the deterioration of low-frequency characteristics
even when the difference between the Ps
d [mean_Ps] and the Ps
d [same_Ps] is relatively large, Ps is estimated using the weighted_mean_Ps system.
Ps
d (Ps
d [weighted_mean_Ps]) in the weighted_mean_Ps system can be represented as a following
expression (13), when "W" indicates a weight. The terms in expression (13) are functions
of frequencies. The weight W is determined so that the weighting of the same_Ps becomes
large in a low frequency.
[0113] FIG. 35 is a graph showing one example of a weighting function in the weighted_mean_Ps
system.
[0114] As shown in FIG. 35, the weight W in Expression (13) is a function of a frequency
k for the FTWF. Using a 1D Hanning function, the weight W is given as a following
expression (14).
[0115] As a first modification of the weighted_mean_Ps system, there is a method in which
the weight W in Expression (13) is determined based on the difference between an image
and the Ps to optimally control the weighting. For example, the weighting is dynamically
controlled based on the degree of a difference E (Ps
d difference) between the Ps
d [mean_Ps] and the Ps
d [same_Ps], represented as a following expression.
[0116] A difference in high frequency components which is caused by a difference in the
noise can occur between the Ps
d [mean_Ps] and the Ps
d [same_Ps]. Thus, since it is desired to increase the difference in low-frequency
components, the Ps
d difference E is determined after performing smoothing or is determined using an average
in a certain low-frequency range.
[0117] Further, the Ps
d difference E may be a function in filter space k and may be a value obtained by averaging
with respect to k. In short, for a larger Ps
d difference E, the weighting of the Ps
d [same_Ps] is increased.
[0118] FIG. 36 is a graph showing a second modification of the weighting function in the
weighted_mean_Ps system.
[0119] Using a threshold Eth of the Ps
d difference E, w is determined so as to satisfy the following expression (15). Additionally,
as shown in FIG. 36, expressions (14) and (15) are combined, and the weighing W in
expression (13) is expressed by a function (W2(k)) of a frequency k as a following
expression (16).
[0120] In Expression (16), For E = 0, W is equal to 0. In this case, based on Expression
(13), the Ps
d [weighted_mean_Ps] is generated using only the Ps
d [mean_Ps]. Similarly, in Expression (16), For "E = Eth", W is equal to "1". In this
case, based on Expression (13), the Ps
d [weighted_mean_Ps] is generated using only the Ps
d [same_Ps].
[0121] The method for estimating the Ps by using the weighted_mean_Ps is particularly effective
for data having larger difference between the image and the Ps. For example, the method
is effective for cases in which the contrast is inverted, such as cases of T1, T2,
and proton in MRI. Although the description in the present invention has been given
of a case in which image data obtained by the diagnostic imaging apparatus is subjected
to recovery processing performed by the WF, the present invention is not limited to
medical images. For example, the present invention is applicable to any image data
that contain noise of commercially available digital TVs, digital cameras, and so
on. In addition, the present invention is applicable to digital multimedia in various
fields for processing not only image data but also audio signals and so on.
[0122] According to the data processing system, the data processing method, the diagnostic
imaging apparatus, and the magnetic resonance imaging apparatus of the present invention,
in an application to processing-target data having actual noise, the SNR is improved
while spatial frequency components of the processing-target data are reserved as much
as possible, and an adequate WF having a large improvement effect of characteristic
deterioration allows data processing to be appropriately performed.
[0123] It is explicitly stated that all features disclosed in the description and/or the
claims are intended to be disclosed separately and independently from each other for
the purpose of original disclosure as well as for the purpose of restricting the claimed
invention independent of the composition of the features in the embodiments and/or
the claims. It is explicitly stated that all value ranges or indications of groups
of entities disclose every possible intermediate value or intermediate entity for
the purpose of original disclosure as well as for the purpose of restricting the claimed
invention, in particular as limits of value ranges.
1. A data processing system comprising:
a signal-power estimating unit for estimating signal power by using reference data
containing data different from processing-target data; and
a data processing unit for processing the processing-target data by using a WF based
on the signal power estimated by the signal-power estimating unit.
2. The data processing system according to claim 1, wherein the signal-power estimating
unit estimates the signal power by using the reference data arranged in Fourier space
and the data processing unit processes the processing-target data arranged in the
Fourier space by using the WF based on the signal power estimated by the signal-power
estimating unit.
3. The data processing system according to claim 1, wherein the signal-power estimating
unit estimates the signal power by using the reference data arranged in FREBAS space
and the data processing unit processes the processing-target data arranged in the
FREBAS space by using the WF based on the signal power estimated by the signal-power
estimating unit.
4. The data processing system according to claim 1, further comprising image reconstructing
unit for reconstructing an image in real space by subjecting the processing-target
data, processed by the data processing unit, to two-dimensional or three-dimensional
Fourier transform for each set of data.
5. The data processing system according to claim 4, further comprising synthesis and
difference-operation processing unit for performing synthesis processing and/or difference-operation
processing on the image reconstructed by the image reconstructing unit.
6. The data processing system according to claim 5, wherein the synthesis processing
comprises addition processing for each pixel or maximum intensity projection processing.
7. A data processing method comprising steps of:
(A) estimating signal power by using reference data containing data similar to processing-target
data; and
(B) processing the processing-target data by using a WF based on the signal power
estimated in the step of (A).
8. The data processing method according to claim 7, wherein, in the step of (A), the
signal power is estimated using the reference data arranged in Fourier space, and
in the step of (B), the processing-target data arranged in the Fourier space is processed
using the WF based on the signal power estimated by the step of (A).
9. The data processing method according to claim 7, wherein, in the step of (A), the
signal power is estimated using the reference data arranged in FREBAS space, and in
the step of (B), the processing-target data arranged in the FREBAS space is processed
using the WF based on the signal power estimated by the step of (A).
10. The data processing method according to claim 7, wherein the reference data different
from the processing-target data is acquired.
11. The data processing method according to claim 7, wherein when multiple pieces of reference
data are acquired, the signal power is determined by averaging the multiple pieces
of reference data.
12. The data processing method according to claim 7, wherein when multiple pieces of reference
data are acquired, the signal power is determined by weighted averaging of the multiple
pieces of reference data.
13. The data processing method according to claim 12, wherein the signal power is determined
by increasing the weighting of the processing-target data with respect to a low-frequency
component and by increasing the weighting of either one of the processing-target data
and data different from the processing-target data, the either one having a larger
SNR of the signal power.
14. The data processing method according to claim 7, wherein the WF is computed from:
or
an expression that is substantially equal to either of above two expression, wherein
the "Ps" indicates the signal power, the "Pn" indicates noise power, and the "a" indicates
a parameter for controlling an estimated ratio between the "SNR" and the noise power
Pn.
15. The data processing method according to claim 7, wherein the WF is computed from:
or
an expression that is substantially equal to either of above expression, wherein the
"Ps" indicates the signal power, the "Pn" indicates noise power, the "H" indicates
a deterioration characteristic, and the "*" indicates a complex conjugate.
16. The data processing method according to claim 7, wherein data before injection of
a contrast agent is acquired, data after the injection of the contrast agent is compared
with the data before the injection of the contrast agent and is acquired as the processing-target
data with a reduced an SNR, and the data before the injection of the contrast agent
is used as the reference data.
17. The data processing method according to claim 7, wherein data before injection of
a contrast agent is acquired, data after the injection of the contrast agent is compared
with the data before the injection of the contrast agent and is acquired as the processing-target
data with a reduced an SNR, all time-series data including the data before the injection
of the contrast agent and the data after the injection of the contrast agent are averaged,
and the averaged data is used as the reference data.
18. The data processing method according to claim 7, wherein the reference data are different
from each other for data distinguished between after and before a required stimulus.
19. The data processing method according to claim 18, wherein for an f-MRI, the reference
data comprises two pieces of reference data for respective two sets of data having
different contrasts from each other before load application and after the load application.
20. The data processing method according to claim 7, wherein the reference data comprises
image data having different types of parameters.
21. The data processing method according to claim 7, wherein the reference data is obtained
by averaging all or part of a group of data.
22. The data processing method according to claim 21, wherein the group of data comprises
data obtained after motion is corrected.
23. The data processing method according to claim 7, wherein the reference data comprises
data obtained by averaging a group of data along an MPG axis.
24. A diagnostic imaging apparatus comprising:
a signal-power estimating unit for estimating signal power by using reference data
containing data different from processing-target data;
a data processing unit for processing the processing-target data by using a WF based
on the signal unit estimated by the signal-power estimating unit;
an image reconstructing unit for reconstructing an image in real space by subjecting
the processing-target data, processed by the data processing unit, to two-dimensional
or three-dimensional Fourier transform for each set of data; and
a synthesis and difference-operation processing unit for performing synthesis processing
and/or difference-operation processing on the image reconstructed by the image reconstructing
unit.
25. The diagnostic imaging apparatus according to claim 24, wherein the signal-power estimating
unit estimates the signal power by using the reference data arranged in Fourier space
and the data processing unit processes the processing-target data arranged in the
Fourier space by using the WF based on the signal power estimated by the signal-power
estimating unit.
26. The diagnostic imaging apparatus according to claim 24, wherein the signal-power estimating
unit estimates the signal power by using the reference data arranged in FREBAS space
and the data processing unit processes the processing-target data arranged in the
FREBAS space by using the WF based on the signal power estimated by the signal-power
estimating unit.
27. A magnetic resonance imaging apparatus comprising:
an image capture section for generating magnetic resonance signals by applying gradient
magnetic fields and high-frequency pulses to a subject in a static magnetic field;
a high-frequency coil for detecting the magnetic resonance signals;
an acquiring unit for arranging the magnetic resonance signals, detected by the high-frequency
coil, in frequency-band-split space;
an image generating unit for generating multiple images by reconstructing data arranged
in the frequency-band-split space by the acquiring unit; and
a filter for using, as signal power in frequency-band-split space of the processing-target
image, signal power of data in frequency-band-split space in which, an image of the
multiple images which has a highest similarity to a processing-target image, wherein
a gain is increased for a band in which an SNR is higher in the frequency-band-split
space and the gain is reduced for a band in which the SNR is lower.